Abstract/Description

More than 2 billion people live on less than 2 US dollars per day. People in these conditions often have inadequate access to basic sanitation, safe water, and medical services. These individuals, households and communities may be at high risk for a wide range of preventable and treatable infectious diseases. The aims of this study were to: 1) describe the individual- and household-level prevalence of a number of endemic helminth, protozoal, bacterial and viral infections of people in a small-holder farming community in western Kenya; 2) explore the spatial distribution of risk of infection for this wide range of pathogens; 3) quantify the association between social and environmental conditions and individualand household-level risk of infection; 4) identify shared risk factors for household-level infection. All data were collected between July 2010 and July 2012 as part of a cross-sectional survey of 416 households containing 2113 people. This sample was considered to be representative of a population of 1.4 million people living in a rural, mixed farming area of western Kenya that is characterised by high levels of poverty. Sampled individuals were tested for exposure to, or infection with, at least 21 infectious agents using a range of faecal, blood and serological tests. Extensive questionnaire-based data were also collected at both the individual and household level. The spatial distribution of infectious disease risk was assessed using a kernel smoothing approach, with spatial congruence in areas of elevated risk for multiple pathogens examined as a potential indicator of shared contextual or compositional effects. Individual- and household-level risk factors for infection with a range of prevalent pathogens were explored using multilevel logistic regression, with a particular focus on examining the impact of socioeconomic position (SEP). A hierarchical multispecies zero-inflated binomial (ZIB) regression was used to derive an estimate of household ‘species richness’ for six neglected helminth infections (viz. Ascaris lumbricoides, hookworm, Trichuris trichiura, Schistosoma mansoni, Taenia solium, and Strongyloides stercoralis) with correction for detection error. This modelling framework also allowed assessment of the relationship between household-level infection with each parasite and a range of social and environmental conditions, and, uniquely for a single study setting, the average response of the ‘group’ of parasites to these conditions. This study found very high levels of parasitism in the community, particularly with hookworm (individual survey-adjusted prevalence: 36.3% (95% CI 32.8 – 39.9)), Entamoeba histolytica/dispar (30.1% (27.5 – 32.8)), Plasmodium falciparum (29.4% (95% CI 26.8 – 32.0)), and Taenia spp. (19.7% (16.7 – 22.7)). Some degree of within-household clustering of infection was found for all pathogens under study, and this was particularly large for the helminth species (intra-cluster correlation coefficient (ICC) for A. lumbricoides = 61.8%; T. trichiura = 54.1%; Taenia spp. = 48.0%; Hookworm = 35.3%) and HIV (27.2%). Virtually all of the (map-able) pathogens showed spatial heterogeneity in disease risk, with evidence of spatial clustering in household-level infection for most infectious agents, notably HIV, S. mansoni, A. lumbricoides, T. trichiura, P. falciparum and hookworm. There was substantial overlap in spatial clusters for several infections, with some evidence of a geographic gradient in risk for multiple pathogens in the study area. This included significant spatial clustering of household-level polyparasitism in the south-west of the study area, towards lake Victoria. A socioeconomic gradient was identified, even in this predominantly poor, rural community. Increasing socioeconomic position (SEP) resulted in significantly reduced risk of infection for several infectious agents, notably E. histolytica/dispar (where an average person in the poorest household had a predicted probability of 0.38 of infection, whilst an average person in the richest household had a predicted probability of infection of 0.20), P. falciparum (0.49 vs. 0.21), and hookworm (0.54 vs. 0.10). By contrast, individuals living in the richest households were at significantly elevated risk for TB (0.027 vs. 0.09). Individuals living in the poorest households were least likely to report the recent use of medical treatments (antibiotics, anti-inflammatories and antimalarials). On the basis of the observed data, the average species richness (out of 21 unique species) per household was 4.7 (95% CI 4.4 – 4.9), with a range of 0 to 13. The average number of neglected helminth species observed in each household (out of a maximum of 6) was 1.74, and ranged from 0 to 5. Following correction for detection error (using the multi-species ZIB regression), the predicted average helminth species count was 3.0, with a range from 0.94 to 5.96. Whilst socioeconomic position had little effect on the probability that a household was infected with any of the neglected helminths of interest, individual infection, or more intense infection, appeared to be more likely in poorer households for several helminth species (viz. hookworm, S. mansoni, A. lumbricoides and weaker evidence for S. stercoralis). Interestingly, household-level helminth species richness was identified as a significant positive predictor of individual risk of HIV infection, raising potentially interesting questions about helminth-HIV interactions in the study area. This study has integrated approaches from epidemiology and ecology to explore infectious disease risk and its determinants at a range of biological, social, and geographical scales in western Kenya. The research has indicated that there are broad geographic trends in risk for multiple infections within this community, and has been able to identify potentially interesting associations between a range of endemic pathogens. Household-level effects were identified as being important, and explain much of the variation in individual risk of infection, particularly with helminth species. Using zero-inflated binomial modeling approaches, this study was able to show that considering the presence or absence of infection at the household-level can be a useful outcome, which can provide information that is not necessarily captured by focusing on outcomes at the individual level only. The high prevalence of polyparasitism and the probable existence of shared risk factors for multiple infectious agents should be taken into account in the design of integrated disease control programmes.